Computation Prediction of Drug Response Based on Omics Data

• Conditions: Breast Cancer
• Interventions: Other: virtual anti-cancer drug

• Registration Number: NCT05833802
• Lead Sponsor: Peking University Cancer Hospital & Institute

Brief Summary

The goal of this observational study is to assess the performance of computational medicine technology in predicting patients response to anticancer drugs based on omics data.The main question it aims to answer is test consistency between the computing drug response and the response of real-world clinical trials. Participants will take part in silico.

Detailed Description

A companion trial in silico was planned to compare head-to-head with a real clinical study of anti-tumor registered new drugs to verify the consistency between the efficacy prediction results of virtual clinical studies and the efficacy results of traditional clinical trials.

Subjects simultaneously entered real world clinical trials and virtual clinical trials built by computer modeling and artificial intelligence technology. The results of traditional clinical trials were compared with those of virtual clinical trials to calculate the consistency of virtual clinical trials.

By predicting the population with consistent efficacy, locking the response population to new drugs, using the innovative technology of computational medicine, grasping the omics characteristics of the response population, and using this as a starting point to determine the target population of clinical trials, so as to determine new screening conditions, design new clinical trials, accurately match the effective population, and revolutionary change the efficiency of clinical trials, thereby shortening the process and cost of clinical trial development.

Study Timeline

• Study Start: 2023-02-15
• Status Verified: 2023-03
• Study First Submitted: 2023-03-24
• Study First Submitted QC: 2023-04-25
• Last Update Submitted: 2023-04-25
• Study First Posted: 2023-04-27
• Last Update Posted: 2023-04-27
• Primary Completion: 2024-05-15
• Study Completion: 2024-09-15

Recruitment & Eligibility

• Status: ENROLLING_BY_INVITATION
• Sex: All
• Target Recruitment: 25

Inclusion Criteria

1. clinical diagnosis of triple-negative breast cancer
2. The subjects agreed to participate in the traditional clinical trial and signed informed consent.
3. The subjects agreed to participate in the virtual study and signed informed consent.

Exclusion Criteria

1. Subjects do not meet the inclusion criteria of traditional clinical trial.
2. Subjects suffered from other cancer disease

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
the virtual cohort	virtual anti-cancer drug	the virtual cohort that enroll in silico clinical trial (ISCT), and will be treated by virtual anti-cancer drug.

Primary Outcome Measures

Name	Time	Method
consistency	8 weeks after the first administration of the drug for subjects	To compare the consistency of the tumor response between two cohorts. Tumor response for Patients in traditional clinical trial cohort will be assessed by New response evaluation criteria in solid tumours v1.1. Tumor response for virtual patients in virtual study will be predicted by the trained model.The efficacy prediction model will be trained using 4-5 patients evaluated for tumor response according to New response evaluation criteria in solid tumours v1.1, including at least 2 patients with Complete Response or Partial Response . The training of this model is based on the Damage Assessment of Genomic Mutations algorithm（EBioMedicine. 2021 Jul;69:103446）with the input of patients' genomic data.

Trial Locations

Locations (1)

Shuhua Zhao; 🇨🇳 Beijing, Beijing, China